Beyond Chess: Deep Green Models Rapid Change for Combat Commanders

Can an artificial intelligence program anticipate military
surprises? The USC Information Sciences Institute is playing
a $5.9 million part in a DARPA research effort called Deep
Green aimed at creating a system that can do so, one that
might help future combat commanders in the field anticipate
enemy moves.

The same system might look around and recruit additional
computing resources if the situation were too dire, the
problem too difficult.

The Deep Green program, a next-generation battle
command and decision support technology, is the vision of
Col. John Surdu, who manages the program for the
Information Processing Techniques Office of DARPA.

The system interleaves anticipatory planning with adaptive
execution to help the commander think ahead, identify when
a plan is going awry, and prepare options before they are
needed.

Deep Green will use a human operator's hand drawn
sketches and words to induce intent. It will generate options
for all sides in an operation and predict the likelihood of
multiple futures.

By presenting decisions early and allowing the commander
to "see the future," Deep Green supports commander's
visualization and adaptive execution, enabling correct, timely
decisions by the commander.

Deep Green has several components, including novel
interfaces for getting guidance from and presenting options
to commanders, powerful simulations of the battlespace, and
methods for efficiently searching the space of future options.
The prime contractor, responsible for all these elements, is
SAIC.

ISI researcher Paul Cohen (above, with collaborator Yu-Han Chang), heading one of the two ISI
groups subcontracting on the program, notes that the name
is meant to recall Deep Blue, the famous IBM chess playing
program that defeated world champion Garry Kasparov in a
1997 match, a landmark in the history of artificial
intelligence.

"But chess is a special, artificial situation," Cohen (left)
notes. "The pieces occupy fixed positions for long intervals,
then move instantaneously." The battlefield is a very
different place, Cohen says. There, units on both sides are in
continuous motion. Moreover, chess players can see the
whole board, whereas commanders have limited visibility of
the battlefield.

A program like Deep Blue visualizes where pieces might
move in the future, based on the moves possible for knight,
bishop, and so on. The problem for Deep Green is that time
and location change continuously, so the very notion of a
"state of the board" needs a new formulation.

Cohen, deputy director of ISI's Intelligent Systems Division
and director of the ISI Center for Research on Unexpected
Events is working with Yu-Han Chang on a $6 million
segment of the effort. The pair are creating tools by
analyzing a digital last-man-standing free-for-all called
"Arena War."

Arena War screenshotclick on image for larger
view

Chang and Cohen's program, called Adversarial Continuous
Time and Space Search, (ACTSSpoint), represents collections of
interacting combatants (units) by what are called "fluents", a
concept close to the time-space operators called vectors
familiar to first year physics students.

Fluents represent periods in which activities of the units
modeled don't conflict or interfere with each other, or
complete their mission or arrive at their goal. When they
do, a decision point is reached, where new vectors have to
be assigned, creating new fluents.

"Rather than relying on copious amounts of sampling to
estimate future outcomes," reads a report presented by
Chang, Cohen and Wesley Kerr in November 2007, "fluents
take advantage of process models that can either be solved
in closed form or can be efficiently updated recursively."

The ACTSS system aids a human commander in the Arena
War by "generating, evaluating and monitoring possible
futures. It identifies potential critical points in these futures,
and ranks the options for possible next actions."

In the words of the report: "To play Arena War with the help
of the ACTSS system, the commander [i.e., human
operator] first inputs his plan of attack, as well as his
expectations about the actions his opponents will take," This
can either be in the form of a list of specific actions "first go
to point A, then to point B," or by programming simple
instructions into the pieces, such as "move away from pieces
you see trying to move toward you."

Trouble. Commander learns that current fluents add up
to critical threat

"With the plans inputted, the commander can then start the
game and the ACTSS system will immediately generate
updated Futures Graphs at fixed intervals." The graphs look
ahead in time, detailing how successions of fluents could
develop from the fluents in play at the beginning.

And the ACTSS system uses these look-ahead graphs to see
whether the commander's forces are in danger of what
would in chess be check, and does so soon enough for the
commander to change activity to counter the threat.
"Moreover, the look-ahead is very efficient," says the report.

Promising as the opening is, says Davis, "we feel the goals of Deep
Green can only be met by optimizing the use of remote
computational assets. It is clear that the warfighter could
use more compute capability than can be carried into the
battlespace."

Enter a parallel supporting effort by Robert F. Lucas, director
of ISI's computational science division and Dan Davis aimed
at finding ways to put the huge computational resources
necessary to solve complicated fluents problems into a
system that could actually be used in chaotic wartime
conditions — "in a tent," says Davis.

Davis and Lucas are working on a $1.6 million contract trying
to create a system that links to portable electronics; a very
efficient, bandwidth-saving, distributed computing platform,
and an effective method for assessing local computation and
communications limitations.

If it can be done - if they can create a very large trans-
globally distributed computer network that still requires very
little bandwidth, the Deep Green system can be made
scalable — "it will run effectively on one processor to twenty
processors on scene, or hundreds within the battlespace, or
thousands across the globe," explains Lucas.

"This capability means that the commander will never be
without some assistance, no matter the communications
situation, but can have the power of remote computers,
when conditions permit," he continued.

Davis and Lucas (right) earlier worked on war-game models
of unprecedented scale, involving millions of autonomous
units moving in continent-scale environments, assembling
computer resources from across the country. Key advances
realized in that effort, included complementary routers (Web
and Tree) that could integrate different simulation modes.
This work will be integrated into the Deep Green effort.

ISI researchers acknowledge that many problems remain to
be solved. "But we already can play a mean game of Arena
War," says Cohen.